Ontology learning for Semantic Web Services

Abstract

The expansion of Semantic Web Services is restricted by traditional ontology engineering methods. Manual ontology development is time consuming, expensive and a resource exhaustive task. Consequently, it is important to support ontology engineers by automating the ontology acquisition process to help deliver the Semantic Web vision. Existing Web Services offer an affluent source of domain knowledge for ontology engineers. Ontology learning can be seen as a plug-in in the Web Service ontology development process, which can be used by ontology engineers to develop and maintain an ontology that evolves with current Web Services. Supporting the domain engineer with an automated tool whilst building an ontological domain model, serves the purpose of reducing time and effort in acquiring the domain concepts and relations from Web Service artefacts, whilst effectively speeding up the adoption of Semantic Web Services, thereby allowing current Web Services to accomplish their full potential. With that in mind, a Service Ontology Learning Framework (SOLF) is developed and applied to a real set of Web Services. The research contributes a rigorous method that effectively extracts domain concepts, and relations between these concepts, from Web Services and automatically builds the domain ontology. The method applies pattern-based information extraction techniques to automatically learn domain concepts and relations between those concepts. The framework is automated via building a tool that implements the techniques. Applying the SOLF and the tool on different sets of services results in an automatically built domain ontology model that represents semantic knowledge in the underlying domain. The framework effectiveness, in extracting domain concepts and relations, is evaluated by its appliance on varying sets of commercial Web Services including the financial domain. The standard evaluation metrics, precision and recall, are employed to determine both the accuracy and coverage of the learned ontology models. Both the lexical and structural dimensions of the models are evaluated thoroughly. The evaluation results are encouraging, providing concrete outcomes in an area that is little researched.